Artificial intelligence AI is a new technical science for researching and developing theories, methods, technologies and application systems for simulating, extending and expanding human intelligence. Artificial intelligence is a branch of computer sciences and attempts to learn about the essence of intelligence, and produces a new intelligent machine capable of responding in a manner similar to human intelligence. The studies in the field comprise robots, language recognition, image recognition, natural language processing, expert systems and the like.
Natural interaction of a computer and a human being is a kernel issue of artificial intelligence. To enable the machine and the human being to communicate without any hindrance, a problem to be solved first is to enable the machine to understand language spoken by the human being.
At present, human-machine dialog products are applied more and more widely. Different from the query form of search, query in the human-machine dialog products usually appear in the form of speech. This form better complies with human natural expression. However, diversity and complexity of speech expressions increases the machine's difficulty in understanding such type of queries.
In current main human-machine dialog products, a main function is usually is divided into different application domains, for example, booking train tickets is one application domain, and weather query is also an application domain.
A procedure of the machine understanding a spoken query may take the form that given a certain query, the machine needs to parse whether it belongs to a certain application domain, and if yes, needs to provide an intent of the query and parameter information under the intent.
For example, there are two queries: query=help me to book a train ticket from Beijing to Tianjin, and query=I want to go to Tianjin from Beijing by train. The two queries both convey a user's intent to “book a ticket”. The parameter information includes: departure: Beijing, and destination: Tianjin.
It can be seen that a key problem in human-machine dialog products is paring the user's query and thereby obtaining an accurate parsing result.
In the prior art, the following parsing manner is usually employed: summarize some cases manually, find fixed templates of these cases, store the templates, and when a query needs to be parsed, match the query with the stored templates, and determine the intent of the query and the parameter information under the intent.
However, speech expression manners are diverse and complicated. The templates needs to be summarized manually and are usually in a limited number. Therefore, the templates can only be used to parse some simpler queries that can be covered by the templates, and cannot correctly parse other queries, i.e., generally speaking, parsing results are less accurate.